Navigating today's database scaling options can be a nightmare. Explore the compromises involved in both traditional and new architectures.

MongoDB is an object-oriented, simple, dynamic, and scalable NoSQL database. It is based on the NoSQL document store model. The data objects are stored as separate documents inside a collection — instead of storing the data into the columns and rows of a traditional relational database. The motivation of the MongoDB language is to implement a data store that provides high performance, high availability, and automatic scaling. MongoDB is extremely simple to install and implement. MongoDB uses JSON or BSON documents to store data. General distributions for MongoDB support Windows, Linux, Mac OS X, and Solaris.

Terminology and Concepts

If you're not familiar with MongoDB, here's a quick translation cheat sheet to get you familiar with the terminology.

SQL Server

MongoDB

Database

Database

Table

Collection

Index

Index

Row

Document

Column

Field

Joining

Linking & Embedding

Partition

Sharding (Range Partition)

Replication

ReplSet

Making the Choice

Of course, your choice of database is always a decision based on pros and cons.

Pros

Document oriented

High performance

High availability — Replication

High scalability – Sharding

Dynamic — No rigid schema.

Flexible – field addition/deletion have less or no impact on the application

Heterogeneous Data

No Joins

Distributed

Data Representation in JSON or BSON

Geospatial support

Easy Integration with BigData Hadoop

Document-based query language that’s nearly as powerful as SQL

Cloud distributions such as AWS, Microsoft, RedHat,dotCloud and SoftLayer etc:-. In fact, MongoDB is built for the cloud. Its native scale-out architecture, enabled by ‘sharding,’ aligns well with the horizontal scaling and agility afforded by cloud computing.

Cons

A downside of NoSQL is that most solutions are not as strongly ACID-compliant (Atomic, Consistency, Isolation, Durability) as the more well-established RDBMS systems.

Complex transaction

No function or stored procedure exists where you can bind the logic

Implementation

Good For:

E-commerce product catalog.

Blogs and content management.

Real-time analytics and high-speed logging, caching, and high scalability.

Configuration management.

Maintaining location-based data — Geospatial data.

Mobile and social networking sites.

Evolving data requirements.

Loosely coupled objectives — the design may change by over time.

Not so Good For:

Highly transactional systems or where the data model is designed up front.

Tightly coupled systems

And there you have it! Now, you've got a quick and easy overview of how MongoDB works, some use cases where it can shine, and how it relates to SQL technology.